tpptrFTest: Analyze spline fits to detect differential behavior over time
In DoroChilds/TPP: Analyze thermal proteome profiling (TPP) experiments

Description Usage Arguments Details Value See Also Examples

Analyze fitted natural spline models and look for differential behaviour between conditions by a moderated F-test.

1	tpptrFTest(fittedModels, doPlot = FALSE, resultPath = NULL)

`fittedModels`	a table of fitted spline models (produced by `tpptrFitSplines`).
`doPlot`	boolean value indicating whether QC plots should be produced. Currently, QC plots comprise distributions of the F statistics, and the p-values before/ after Benjamini Hochberg adjustment.
`resultPath`	location where to store QC plots, if `doPlot` = TRUE.

If doPlot is TRUE, but no resultPath is specified, the plots will be prompted to the active device.

The moderated F-statistic is calculated by the following equation: ...

A long table containing the hypothesis test results per protein.

ns, squeezeVar

data(hdacTR_smallExample)
tpptrData <- tpptrImport(configTable = hdacTR_config, data = hdacTR_data)
normResults <- tpptrNormalize(data = tpptrData, normReqs = tpptrDefaultNormReqs())
normData_eSets <- normResults$normData
fitData <- tpptrTidyUpESets(normData_eSets)
fits <- tpptrFitSplines(data = fitData, factorsH1 = "condition", nCores = 1, splineDF = 4:5)
testResults <- tpptrFTest(fittedModels = fits)